**Mathematical Optimization Models in the Sugarcane Harvesting Process Mathematical Optimization Models in the Sugarcane Harvesting Process**

DOI: 10.5772/intechopen.71530

Fernando Doriguel, Carlos Alexandre Costa Crusciol and Helenice de Oliveira Florentino Fernando Doriguel, Carlos Alexandre Costa Crusciol and Helenice de Oliveira Florentino Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.71530

#### **Abstract**

[29] Leite JM. Eficiência agronômica da adubação nitrogenada associada à aplicação de substâncias húmicas em cana-de-açúcar. Tese (Doutorado em ciências área de concentração solos e nutrição de plantas). Piracicaba – SP: Escola Superior de Agricultura Luiz de

[30] Mauad M, Crusciol CAC, Filho HG, Machado SR. Silica deposition and rate the nitrogen is silicon in rice. Semina: Ciências Agrárias, Londrina. 2013;**34**(4):1653-1662. DOI: 10.5433/

[31] Savvas D, Ntatsi G. Biostimulant activity of silicon in horticulture. Scientia Horticulturae.

Queiroz; 2016 132 fls

206 Sugarcane - Technology and Research

1679-0359.2013v34n4p1653

2015;**196**:66-81. DOI: 10.1016/j.scienta.2015.09.010

Over the past few decades, due to environmental and economic factors, the sugarcane has been considered a versatile and important plant to the several countries. The energysugar-ethanol agro-industries are seeking to take advantage of all its material, with the main products produced being renewable energy, sugar and ethanol. In this chapter, we propose to present a review of the important works that use mathematical and computational tools, aiming to optimize the sugarcane harvesting, in the past 30 years.

**Keywords:** economy, mathematical models, optimization, sugarcane, mechanized harvesting

### **1. Introduction**

A number of environmental and economic benefits are claimed for sugarcane. Currently, the ethanol is the most widely used biofuel for transportation worldwide. Production of ethanol from sugarcane is one way to reduce consumptions of both crude oil and environmental pollution. In addition to ethanol, sugar and renewable energy can also be produced from sugarcane. In this way, sugarcane is considered one of the most important industrial cash crops of the world.

On the other hand, there is a great concern about some factors related to the sugarcane production, for example, to increase the processing capacity of the large volume of sugarcane; control the pollution; improve the sugar content and quality of the harvested cane crop; reduce the losses and to increase the volume of the load in the transshipments and trucks. In this context, much research has been carried out in an attempt to improve the

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

production process in this sector, especially with regard to the sugarcane harvesting [1]. Many studies have been performed in the sugar-energy sector using mathematical and computational modeling techniques in the mechanized harvesting planning. These studies present methodologies to optimize the sugarcane harvesting planning aiming to maximize sugarcane production; minimize costs related to harvesting; minimize the number of maneuvers of the harvester machine; optimize routes for the transport of machines and trucks and many others. The mathematical tools use continuous, discrete and heuristic optimization techniques [2–7].

investments, however, increases production and significantly diminishes labor require-

Mathematical Optimization Models in the Sugarcane Harvesting Process

http://dx.doi.org/10.5772/intechopen.71530

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The fuel crisis (the search for alternative fuel sources, for example, ethanol) and environmental (reduction of burning in sugarcane plantations), social (labor issues) and economic issues led other countries to join the mechanized harvesting system from the 1990s. In this way, the mechanized harvesting was introduced in the scenario of the sugarcane industry. After that, many and great improvements have been observed, such as the increasing volume of the sugarcane harvested, the industry became able to meet the demands, studies have been made aiming the performance improvement of the machinery and equipment, and the pollution generated by the pre-harvest burning of sugarcane has been reduced. Therefore, the mechanized harvesting grew in synchrony with the technological evolution, forced by the demand of the consumer market and the environmental impositions [15–17].

Sugarcane is a semi-perennial crop and is produced in several regions in the world. According to Kim and Dale [18], in the past, the main uses of sugarcane in the world were basically for food manufacturing and seed extraction. Over the years, the sugarcane started to be looked as an energy feedstock rather than a food and this fact made its production grow significantly. The global evolution of the area planted with sugarcane and the amount harvested for mecha-

Brazil has remained the world's largest producer of sugarcane since 1970, followed by India,

ments and costs.

**3. Sugarcane in the world**

China and other countries (**Figure 2**).

**Figure 1.** Sugarcane production in the world [19].

nized and manual harvesting are presented in **Figure 1**.

According to Sethanan and Neungmatcha [2], one of the important aspects to increasing sugarcane mechanized harvesting efficiency is the optimal planning of the harvesting. These authors noted that minimizing the distance traveled during the harvesting and maximizing the sugarcane production, many economic and environmental gains are achieved. However, these are difficult task to implement because there are conflicting objectives that need to be considered simultaneously. Most of these and many other aspects of the sugarcane industry make their management very complex. In addition to the intrinsic knowledge on the part of the managers, the agro-energy industries have sought partnerships with researchers from universities and research centers to assist them in the development of an optimized crop management. In this way, the development of scientific methodologies such as mathematical modeling and operational research (OR) techniques to aid in decision-making has been very important in this area [7, 9].

Based on the above discussions, we propose in this chapter to present a review of the important works that use mathematical and computational tools, aiming to optimize the management of sugarcane, in the past 30 years.

This chapter is structured as follows. Section 2 describes the evolution of mechanized harvesting in the world since the 1940s. Section 3 presents the world scenario for sugarcane production and harvesting. Section 4 describes the types of harvesting in several countries, as well as their advantages and disadvantages. Section 5 presents the relevance of the mathematical optimization models applied to the sugarcane harvesting process.
